About This PhD Project

Project Description

This project is in the area of monitoring the performance of contacting surfaces within components of heavy machines using novel sensing systems (electrostatic sensor arrays ESA) and advanced machine learning techniques. We will study non-stationary signal processing methods and deep neural network models for inference. The project will involve novel algorithm development and experimental validation on specifically constructed rigs. Work is envisaged at Schaeffler testing labs and their adjunct laboratories to show benefits of multi-sensor approach to understanding distress of components/lubricant under test. The PhD would feed into better load capacity determination for bearings for example.

The studentship will be part of much larger £1m EPSRC funded programme looking at sensing and inference in tribological systems with the following research objectives: (1) to miniaturise existing electrostatic sensor Condition Monitoring, nano stress devices and chemical sensing technology (2) to produce them as arrays (3) to embed electronics, and (4) to use machine leaning to understand outputs and develop predictive tools from training data.

Student background: Good first degree in computer science or similar quantitative discipline. Good knowledge of statics and programming desirable.

Funding Notes

This project is in competition with others for funding. This 3 year studentship covers UK tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is £14,777 for 2018/19.

Applicants must be UK residents with no restrictions on how long they can stay in the UK and have lived here for at least 3 years prior to the start of the studentship. This residence cannot be mainly for the purpose of receiving full-time education.